Consulta 3: \u00a0<\/span><\/p>\nWITH dy\r\nAS (\r\n\tSELECT pl.hnum AS holedn\r\n\t\t,pl.ps.Nat AS country\r\n\t\t,(pl.ps.FN || \" \" || pl.ps.LN) AS name\r\n\t\t,pl.ps.ID AS ID\r\n\t\t,array_length(hps.Sks) AS score\r\n\t\t,hpl.hole AS `hole`\r\n\t\t,hpl.day AS `day`\r\n\tFROM (\r\n\t\tSELECT meta(usopen).id AS hnum\r\n\t\t\t,ps\r\n\t\tFROM usopen USE keys \"holes:1:1\" unnest Ps AS ps\r\n\t\t) pl\r\n\tINNER JOIN (\r\n\t\tSELECT TONUMBER(split(meta(usopen).id, \":\") [1]) AS `hole`\r\n\t\t\t,TONUMBER(split(meta(usopen).id, \":\") [2]) AS `day`\r\n\t\t\t,hps\r\n\t\tFROM usopen unnest Rs AS rs unnest rs.Hs AS hs unnest hs.HPs AS hps\r\n\t\t) hpl ON (pl.ps.ID = hps.ID)\r\n\t)\r\n\t,dx\r\nAS (\r\n\tSELECT d.name\r\n\t\t,sum(CASE \r\n\t\t\t\tWHEN d.day = 1\r\n\t\t\t\t\tTHEN d.score\r\n\t\t\t\tELSE 0\r\n\t\t\t\tEND) R1\r\n\t\t,sum(CASE \r\n\t\t\t\tWHEN d.day = 2\r\n\t\t\t\t\tTHEN d.score\r\n\t\t\t\tELSE 0\r\n\t\t\t\tEND) R2\r\n\t\t,sum(CASE \r\n\t\t\t\tWHEN d.day = 3\r\n\t\t\t\t\tTHEN d.score\r\n\t\t\t\tELSE 0\r\n\t\t\t\tEND) R3\r\n\t\t,sum(CASE \r\n\t\t\t\tWHEN d.day = 4\r\n\t\t\t\t\tTHEN d.score\r\n\t\t\t\tELSE 0\r\n\t\t\t\tEND) R4\r\n\t\t,sum(d.score) T\r\n\tFROM dy AS d\r\n\tGROUP BY d.name\r\n\tORDER BY d.name\r\n\t)\r\nSELECT d2.name\r\n\t,d2.R1\r\n\t,d2.R2\r\n\t,d2.R3\r\n\t,d2.R4\r\n\t,d2.T\r\n\t,RANK() OVER (ORDER BY d2.T + CUT) AS Rank\r\nFROM dx AS d2 \r\nLET CUT = (\r\n\t\tCASE \r\n\t\t\tWHEN (\r\n\t\t\t\t\td2.R1 = 0\r\n\t\t\t\t\tOR d2.R2 = 0\r\n\t\t\t\t\tOR d2.R3 = 0\r\n\t\t\t\t\tOR d2.R4 = 0\r\n\t\t\t\t\t)\r\n\t\t\t\tTHEN 1000\r\n\t\t\tELSE 0\r\n\t\t\tEND\r\n\t\t)\r\nORDER BY Rank<\/pre>\nObserve que as classifica\u00e7\u00f5es 4, 8, 9, 10 e 11 est\u00e3o faltando por causa do empate nas pontua\u00e7\u00f5es!<\/span><\/p>\nTarefa 4: <\/strong>Agora, vamos descobrir como cada jogador se saiu ap\u00f3s a rodada 1, a rodada 2 e a rodada 3 em compara\u00e7\u00e3o com a rodada final. Usando as fun\u00e7\u00f5es de janela, \u00e9 muito f\u00e1cil fazer desaparecer os marshmallows cobertos com chocolate. <\/span><\/h5>\nConsulta 4:<\/strong> Use a mesma fun\u00e7\u00e3o RANK(), ordenando pela pontua\u00e7\u00e3o de cada dia (dia1, dia1+dia2, dia1+dia2+dia3) em vez de apenas a pontua\u00e7\u00e3o final.\u00a0<\/span><\/p>\nWITH dy AS (\r\n SELECT\r\n pl.hnum AS holedn,\r\n pl.ps.Nat AS country,(pl.ps.FN || \" \" || pl.ps.LN) AS name,\r\n pl.ps.ID AS ID,\r\n array_length(hps.Sks) AS score,\r\n hpl.hole AS `hole`,\r\n hpl.day AS `day`\r\n FROM\r\n (\r\n SELECT\r\n meta(usopen).id AS hnum,\r\n ps\r\n FROM\r\n usopen USE keys \"holes:1:1\" unnest Ps AS ps\r\n ) pl\r\n INNER JOIN (\r\n SELECT\r\n TONUMBER(split(meta(usopen).id, \":\") [1]) AS `hole`,\r\n TONUMBER(split(meta(usopen).id, \":\") [2]) AS `day`,\r\n hps\r\n FROM\r\n usopen unnest Rs AS rs unnest rs.Hs AS hs unnest hs.HPs AS hps\r\n ) hpl ON (pl.ps.ID = hps.ID)\r\n),\r\ndx AS (\r\n SELECT\r\n d.name,\r\n sum(\r\n CASE WHEN d.day = 1 THEN d.score ELSE 0 END\r\n ) R1,\r\n sum(\r\n CASE WHEN d.day = 2 THEN d.score ELSE 0 END\r\n ) R2,\r\n sum(\r\n CASE WHEN d.day = 3 THEN d.score ELSE 0 END\r\n ) R3,\r\n sum(\r\n CASE WHEN d.day = 4 THEN d.score ELSE 0 END\r\n ) R4,\r\n sum(d.score) T\r\n FROM\r\n dy AS d\r\n GROUP BY\r\n d.name\r\n ORDER BY\r\n d.name\r\n)\r\nSELECT\r\n d2.name,\r\n d2.R1,\r\n d2.R2,\r\n d2.R3,\r\n d2.R4,\r\n d2.T,\r\n DENSE_RANK() OVER (\r\n ORDER BY\r\n d2.T + CUT\r\n ) AS rankMoney,\r\n RANK() OVER (\r\n ORDER BY\r\n d2.T + CUT\r\n ) AS rankFinal,\r\n RANK() OVER (\r\n ORDER BY\r\n d2.R1\r\n ) AS round1rank,\r\n RANK() OVER (\r\n ORDER BY\r\n d2.R1 + d2.R2\r\n ) AS round2rank,\r\n RANK() OVER (\r\n ORDER BY\r\n d2.R1 + d2.R2 + d2.R3 + CUT\r\n ) AS round3rank\r\nFROM\r\n dx AS d2 LET CUT = (\r\n CASE WHEN (\r\n d2.R1 = 0\r\n OR d2.R2 = 0\r\n OR d2.R3 = 0\r\n OR d2.R4 = 0\r\n ) THEN 1000 ELSE 0 END\r\n )\r\nORDER BY\r\n rankFinal,\r\n round1rank,\r\n round2rank,\r\n round3rank<\/pre>\n![\"\"](\"https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/2018\/12\/Screen-Shot-2018-11-29-at-11.18.45-PM-300x112.png\")
<\/p>\n
Agora voc\u00ea pode ver como os jogadores subiram ou desceram a cada dia.<\/span><\/p>\nTarefa 5: Crie o scorecard completo para o l\u00edder usando as estat\u00edsticas b\u00e1sicas de tacada por tacada.<\/strong><\/h5>\nConsulta 5:<\/strong> \u00a0Brooks Koepka \u00e9 o vencedor final do US Open. Vamos obter suas pontua\u00e7\u00f5es, buraco a buraco, e obter as pontua\u00e7\u00f5es cumulativas para ele por rodada. Observe como o agregado simples SUM() e COUNT() funciona como uma fun\u00e7\u00e3o de janela com a cl\u00e1usula OVER().<\/span><\/p>\nSUM(d2.score) OVER (PARTITION BY d2.day ORDER BY d2.hole) hst<\/pre>\nIsso primeiro divide a pontua\u00e7\u00e3o por dia e depois por buraco - especificado pela cl\u00e1usula PARTITION BY, na ordem dos buracos 1-18. Em seguida, o SUM soma as pontua\u00e7\u00f5es at\u00e9 o momento.<\/span><\/p>\nSUM(d3.score) OVER (ORDER BY d3.day,d3.hole) ToTScore<\/pre>\nEssa fun\u00e7\u00e3o SUM() simplesmente soma a pontua\u00e7\u00e3o do dia 1, buraco 1, ao dia 4, buraco 18 - isso \u00e9 especificado pelo ORDER BY d3.day, d3.hole dentro da cl\u00e1usula OVER(). O campo ToTScore mostra o total de shorts do torneio de Koepka em cada buraco.<\/span><\/p>\nWITH dy AS (\r\n SELECT\r\n pl.hnum AS holedn,\r\n pl.ps.Nat AS country,(pl.ps.FN || \" \" || pl.ps.LN) AS name,\r\n pl.ps.ID AS ID,\r\n array_length(hps.Sks) AS score,\r\n hpl.hole AS `hole`,\r\n hpl.day AS `day`,\r\n hpl.Par AS Par\r\n FROM\r\n (\r\n SELECT\r\n meta(usopen).id AS hnum,\r\n ps\r\n FROM\r\n usopen USE keys \"holes:1:1\" unnest Ps AS ps\r\n WHERE\r\n ps.LN = \"Koepka\"\r\n ) pl\r\n INNER JOIN (\r\n SELECT\r\n TONUMBER(split(meta(usopen).id, \":\") [1]) AS `hole`,\r\n TONUMBER(split(meta(usopen).id, \":\") [2]) AS `day`,\r\n hs.Par,\r\n hps\r\n FROM\r\n usopen unnest Rs AS rs unnest rs.Hs AS hs unnest hs.HPs AS hps\r\n ) hpl ON (pl.ps.ID = hps.ID)\r\n),\r\ndx AS (\r\n SELECT\r\n d.name,\r\n d.day,\r\n d.score,\r\n d.hole,\r\n d.Par\r\n FROM\r\n dy AS d\r\n ORDER BY\r\n d.name\r\n),\r\ndz AS (\r\n SELECT\r\n d2.day,\r\n d2.hole,\r\n d2.score,\r\n SUM(d2.score) OVER (\r\n PARTITION BY d2.day\r\n ORDER BY\r\n d2.hole\r\n ) hst,\r\n d2.Par,\r\n SUM(d2.Par) OVER (\r\n PARTITION BY d2.day\r\n ORDER BY\r\n d2.hole\r\n ) hpr\r\n FROM\r\n dx AS d2 LET CUT = (\r\n CASE WHEN (\r\n d2.R1 = 0\r\n OR d2.R2 = 0\r\n OR d2.R3 = 0\r\n OR d2.R4 = 0\r\n ) THEN 1000 ELSE 0 END\r\n )\r\n ORDER BY\r\n d2.day,\r\n d2.hole\r\n)\r\nSELECT\r\n d3.Par,\r\n d3.day,\r\n d3.hole,\r\n d3.hst,\r\n d3.score,(d3.hst - d3.hpr) ToPar,\r\n sum(d3.score) OVER (\r\n ORDER BY\r\n d3.day,\r\n d3.hole\r\n ) ToTScore,\r\n count(1) OVER (\r\n ORDER BY\r\n d3.day,\r\n d3.hole\r\n ) HoleNum\r\nFROM\r\n dz AS d3<\/pre>\n![\"\"](\"https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/2018\/12\/Screen-Shot-2018-12-02-at-11.54.48-AM-300x207.png\")
<\/p>","protected":false},"excerpt":{"rendered":"
Co-author:\u00a0 Sitaram Vemulapalli, Principal Engineer, Couchbase R&D. \u201cThe answer my friend is hiding in JSON” – Bob Dylan There are a lot of public JSON datasets and then is\u00a0awesome JSON datasets. Every company, including yours, has stored a lot of […]<\/p>","protected":false},"author":55,"featured_media":6074,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"inline_featured_image":false,"footnotes":""},"categories":[1815,2294,1816,1819,1812],"tags":[1261,1725],"ppma_author":[8929],"class_list":["post-6055","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-best-practices-and-tutorials","category-analytics","category-couchbase-server","category-data-modeling","category-n1ql-query","tag-json","tag-nosql-database"],"acf":[],"yoast_head":"\n
JSON to Insights: Fast and Easy - The Couchbase Blog<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.couchbase.com\/blog\/pt\/json-to-insights-fast-and-easy\/\" \/>\n<meta property=\"og:locale\" content=\"pt_BR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"JSON to Insights: Fast and Easy\" \/>\n<meta property=\"og:description\" content=\"Co-author:\u00a0 Sitaram Vemulapalli, Principal Engineer, Couchbase R&D. \u201cThe answer my friend is hiding in JSON” – Bob Dylan There are a lot of public JSON datasets and then is\u00a0awesome JSON datasets. Every company, including yours, has stored a lot of […]\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.couchbase.com\/blog\/pt\/json-to-insights-fast-and-easy\/\" \/>\n<meta property=\"og:site_name\" content=\"The Couchbase Blog\" \/>\n<meta property=\"article:published_time\" content=\"2018-12-02T06:08:55+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2025-06-14T01:44:58+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2018\/12\/json-insights.png\" \/>\n\t<meta property=\"og:image:width\" content=\"1000\" \/>\n\t<meta property=\"og:image:height\" content=\"500\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/png\" \/>\n<meta name=\"author\" content=\"Keshav Murthy\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@rkeshavmurthy\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Keshav Murthy\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"10 minutos\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\/\/www.couchbase.com\/blog\/json-to-insights-fast-and-easy\/#article\",\"isPartOf\":{\"@id\":\"https:\/\/www.couchbase.com\/blog\/json-to-insights-fast-and-easy\/\"},\"author\":{\"name\":\"Keshav Murthy\",\"@id\":\"https:\/\/www.couchbase.com\/blog\/#\/schema\/person\/c261644262bf98e146372fe647682636\"},\"headline\":\"JSON to Insights: Fast and Easy\",\"datePublished\":\"2018-12-02T06:08:55+00:00\",\"dateModified\":\"2025-06-14T01:44:58+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\/\/www.couchbase.com\/blog\/json-to-insights-fast-and-easy\/\"},\"wordCount\":883,\"commentCount\":0,\"publisher\":{\"@id\":\"https:\/\/www.couchbase.com\/blog\/#organization\"},\"image\":{\"@id\":\"https:\/\/www.couchbase.com\/blog\/json-to-insights-fast-and-easy\/#primaryimage\"},\"thumbnailUrl\":\"https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2018\/12\/json-insights.png\",\"keywords\":[\"JSON\",\"NoSQL Database\"],\"articleSection\":[\"Best Practices and Tutorials\",\"Couchbase Analytics\",\"Couchbase Server\",\"Data Modeling\",\"SQL++ \/ N1QL Query\"],\"inLanguage\":\"pt-BR\",\"potentialAction\":[{\"@type\":\"CommentAction\",\"name\":\"Comment\",\"target\":[\"https:\/\/www.couchbase.com\/blog\/json-to-insights-fast-and-easy\/#respond\"]}]},{\"@type\":\"WebPage\",\"@id\":\"https:\/\/www.couchbase.com\/blog\/json-to-insights-fast-and-easy\/\",\"url\":\"https:\/\/www.couchbase.com\/blog\/json-to-insights-fast-and-easy\/\",\"name\":\"JSON to Insights: Fast and Easy - 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Previously, he was at MapR, IBM, Informix, Sybase, with more than 20 years of experience in database design & development. He lead the SQL and NoSQL R&D team at IBM Informix. He has received two President's Club awards at Couchbase, two Outstanding Technical Achievement Awards at IBM. 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